Sales is hard, especially in B2B. Conversion rates are low and sales cycle is longer. Average demo to closure rates are in single percentage points and sales cycle is long, likely to be months in enterprise sales. Your customers don’t just take out their credit cards to buy things. They need hand-holding and a lot of validation. You need to make calls, meet them in person, answer their concerns and continue to guide them after sales to ensure that you build a healthy relationship with them. Artificial intelligence can help improve these sales processes. According to a study by Harvard Business Review, companies using AI for sales were able to increase their leads by more than 50%, reduce call time by 60-70%, and realize cost reductions of 40-60%.
Why is using AI in sales important?
As you can see above, interest in AI is increasing. AI brings efficiency and effectiveness to sales thanks to the factors explained below.
How can AI support sales?
As a sales leader, you may be hearing that artificial intelligence will rule the world. You imagine a future where all sales are done by cheap yet effective AI assistants. We are not there yet essentially because AI is not as mature enough to handle complex conversations and relationship building required in sales, therefore, Gartner predicts, only 30% of all B2B companies will employ some kind of AI to augment at least one of their primary sales processes by 2020. AI today does not aim to replace sales reps but acts as an assistant to
- Help them automate repetitive tasks like data entry and meeting scheduling or complicated jobs that do not require personal relationships like sales forecasting
- Enable them to prioritize more effectively and become a better salesperson by highlighting patterns in customer responses
- Provide team leaders with detailed analytics on all communication between sales reps and potential clients including emails, phone calls and chats.
We have identified 15 artificial intelligence use cases and structured these use cases around 4 key activities of today’s sales leaders. We are currently focused on inside sales, for example, a retail sales function has different main activities and therefore different AI use cases. Our framework is by no means comprehensive but it is ever improving so please let us know if you have any comments and suggestions.
Primary sales activities and AI use cases in these activities are:
Forecast sales
Demand forecasting
Forecasts are complicated but automatable. AI allows automatic and accurate sales forecast based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. For more information on AI-powered demand forecasting, feel free to check our article.
Enable sales reps
Better prioritization can enable sales reps to better use their time. Sales reps normally leverage their experience from the last 5-10 years to decide which prospect to focus on. However, AI systems can leverage data from hundreds of sales reps to understand the factors that increase a prospect’s likelihood to buy and help your sales reps focus on the right prospects.
Lead generation
If you like your sales reps, give them leads! Without leads, sales reps spend precious time searching for leads instead of closing deals. For more info, please visit our explanatory article about lead generation.
Predictive sales/lead scoring
After lead generation, it is necessary to determine the priority of leads. These platforms score customers’ likelihood of converting based on 3rd party and company data, allowing your sales reps to prioritize effectively. For more info, please visit our explanatory article about predictive sales.
Another source of data for lead prioritization is your company’s traffic. Website identification tools can help businesses manage the prioritization of leads using how potential customers interact with your company’s digital properties. These tools enable you to identify leads that spend time on the company website and provides company contact information. You define the criteria of how a high-quality lead looks like and then these platforms send “trigger reports” into your sales reps’ inbox automatically.
Sales content personalization and analytics
Once priority customers are decided, sales reps serve them better with sales content personalized to their needs and preferences. Leads’ engagement rate increases with personalized content, businesses convert visitors and retain customers.
Sales rep next action suggestions
AI will analyze your sales reps’ actions and leads will be analyzed to suggest the next best action. No one wants to waste time on email setting up a demo, when they could be closing another deal.
Automate sales activities
Simple activities or activities that do not require relationship building can be automated.
Sales data input automation
AI will synch data from various sources effortlessly and intelligently into your CRM
Sales rep response suggestions
AI will suggest responses during live conversations or written messages with leads
Meeting setup automation (digital assistant)
Leave AI to set up meetings freeing your sales reps time. For example, Calendy links e-mails and conversations to your calendar while Clara responds to your e-mails and organizes your meetings.
Sales rep chat/email bot
Business leaders claim that chatbots can increase sales by 67% on average. This is because a sales chatbot can help break the ice with a personalized message, making it easier for the customer to either engage at that moment, or return to the chat later. AI algorithms can also create customized emails that are specific to a person and helps sales reps outreach prospects without wasting time writing numerous emails.
In-store sales robots
This is mostly relevant in B2C. Physical bots are trialed in various types of stores. Lowe’s has been experimenting with LoweBot in collaboration with Fellow Robotics since 2016. Given the costs and difficulty to replace humans in diverse tasks, it seems that these bots are going to remain niche in the next few years.
AI Avatar
As your sales AI Avatar learns, it gets more intelligent and automatically creates digital marketing interactions with leads. This is another application that can increase the engagement of customers since humans are more comfortable interacting with human-like beings. For example, Dave.ai is AI Avatar vendor that helps businesses visualize home lifestyle products in concept rooms with the help of VR and provides AI-powered recommendations.
Sales analytics & performance manage reps
Sales attribution
Leverage big data to attribute sales to marketing and sales efforts accurately
Customer sales contact analytics
Analyze all customer contacts including phone calls or emails to understand what behaviors and actions drive sales. Share these insights with all your sales force to promote productivity.
Price optimization
Machine learning based dynamic pricing tools scrapes web automatically to gather data on competitors and provides pricing recommendations based on competitors’ pricing information and individual customer’s price perception.
Layout optimization
In B2C retail sales, AI-powered analytics helps businesses optimize in-store/ webpage layout based on customer behavior data.
For more, feel free to read our article on sales analytics.
Now that you know about AI applications in sales, you can read more about these applications in our section on AI in sales. And you can discover all of the latest AI-powered sales assistant software on the market and see how you can bring artificial intelligence into your work environment to make it more efficient and innovative.
And if you have a business problem for which there could be an AI based solution:
